Controllable semantic image editing enables an interactive approach to change the attribute of images with a few clicks, e.g. age, gender, and other general features.
Classic approaches to this task use a Generative Adversarial Net (GAN)[1]} to learn a latent space and suitable latent-space transformations.
However, current approaches often suffer from attribute edits that are entangled, global image identity changes, and the occurrence of image artifacts.
To address these issues, a milestone work, named StyleGAN[2]}, [3]}, proposed to sample the style vectors of generated output from an intermediate latent space \(\mathcal {W}\) , rather than directly from the normal distribution \(\mathcal {Z}\)  as previous works had proposed.
With this work as the backbone, the performance of image editing improved greatly in terms of image quality, image diversity, and attribute accuracy.
However, the attribute accuracy of the edited outcome is highly dependent on the original image input.
As experimental results showed, the edited images tend to have poor attribute accuracy when the attribute combination of the original images is significantly different from that of the desired combination.
For example, this becomes a problem if users need to synthesise an “old man" image from a “young girl.
" Although this issue can be alleviated to some extent by upscaling the factors which control the related attribute intensity, other unrelated attributes will change due to the generation model not being fully disentangled.
Besides, the overly large scaling factors tend to induce significant artifacts in the resulting images.
